Environmental drivers of vector-borne and zoonotic diseases
Leveraging remote sensing for Public Health
About me
- Researcher and lecturer at Instituto Gulich
- Background: Dr. in Biology, MSc. in Spatial Information Applications
- Remote sensing and geospatial applications in disease ecology
- Member of the GRASS GIS Dev Team & project chair; OSGeo Charter member & FOSS4G enthusiast
Overview
- Motivation
- Health Geography
- Disease Ecology
- Leveraging remote sensing for Disease Ecology
- Resolution vs scale
- How can we use RS?
- Examples
- Gaps, challenges and opportunities
- Conclusion
Neglected Tropical Diseases (NTD)
You all have seen this, right?
Health Geography
Environmental health: focuses on environmental hazards, environmental risk assessment, and the physical and psycho-social health impacts of environmental contamination.
Disease ecology: study of infectious diseases (including NTDs) and the spatial distribution of environmental, social, political & economic conditions associated with disease.
Health care delivery and access: spatial patterns of health care provision and patient behavior.
Health geography is the application of geographical information, perspectives, and methods to the study of health, disease, and health care. Mencionar potenciales usos y aplicaciones del SR en los 3 campos
Health Geography
Environmental health: focuses on environmental hazards, environmental risk assessment, and the physical and psycho-social health impacts of environmental contamination.
Disease ecology: study of infectious diseases (including NTDs) and the spatial distribution of environmental, social, political & economic conditions associated with disease.
Health care delivery and access: spatial patterns of health care provision and patient behavior.
While RS has applications in all fields, I’ll focus on those related to disease ecology as it is where I have worked the most
Disease Ecology I
The main objective is to understand the influence of environmental factors and to predict when and where a disease is most likely to occur
decision making, planning of prevention, management or response actions, etc.
Disease Ecology II
- Landscape attributes may influence the level of transmission of an infection
- Spatial variations in disease risk depend not only on the presence and area of critical habitats but also on their spatial configuration
- Disease risk depends on the connectivity of habitats for vectors and hosts
- The landscape is a proxy for specific associations of reservoir hosts and vectors linked with the emergence of multi-host diseases
- To understand ecological factors influencing spatial variations of disease risk, one needs to take into account the pathways of pathogen transmission between vectors, hosts, and the physical environment
- The emergence and distribution of infection through time and space is controlled by different factors acting at multiple scales
- Landscape and meteorological factors control not just the emergence but also the spatial concentration and spatial diffusion of infection risk
- Spatial variation in disease risk depends not only on land cover but also on land use, via the probability of contact between, on one hand, human hosts and, on the other hand, infectious vectors, animal hosts or their infected habitats
- The relationship between land use and the probability of contact between vectors and animal hosts and human hosts is influenced by land ownership
- Human behaviour is a crucial controlling factor of vector-human contacts, and of infection
Use of RS in Health applications
Most common RS variables used
- LST
- Precipitation
- NDVI
- LULC
- Elevation
- NDWI
Remote sensing basic features
However, we should take into account some basic features of remote sensing before selecting which data to use
Remote sensing & scale I
Remote sensing & scale II
How to apply RS in disease ecology?
- To map the response variables, i.e., species occurrence or abundance, infections, disease cases
- To map the predictor variables
- To validate predictions
Let’s have a look at some real cases…
Detecting and mapping species occurrences
- Very high resolution (VHR) imagery
- Hyperspectral data (esp. for plant species)
- Direct and indirect counting
Detecting and mapping species occurrences
Time series analysis of satellite products
- MODIS LST temporal and spatial reconstruction
- Estimation of relevant indices (GRASS GIS temporal framework!)
- Detection of spatial and temporal clusters of favorable conditions for the occurrence of West Nile Fever cases in Greece
Environmental risk of Dengue
- MODIS LST is used to estimate number of extrinsic incubation periods (EIP) that virus might complete; the higher this number, the higher the environmental risk
SDM & GIS based approach for HPS risk map
We combined a rescaled probability map of the host with one of the human cases to determine levels of transmission risk
Cutaneous leishmaniasis and LULCC
Mosquitoes: towards operational high res maps
Spatial distribution of temporal patterns
- Temporal and spatial patterns in Aedes aegypty in Córdoba
- Association with variables derived from Sentinel 2 imagery analysis to predict temporal patterns over the whole city.
Urban environmental characterisation for the distribution of ovitraps
- Object-based classification of VHR imagery
- Landscape metrics for polygons
- Clustering to find groups of similar polygons
- Stratified distribution of ovitraps
MSc thesis, Carla Rodriguez.
Predictive system based on population dynamics and weather forecasting
Development of an early warning system (EWS) for dengue. PhD candidate, Tomás San Miguel.
Online surveillance system
Online surveillance system
Other projects under development
Incidence of asthma as a function of remotely sensed air quality and LULCC. PhD candidate, Abraham Coiman.
Distribution of congenital diseases and access to health. PhD candidate, Carla Rodriguez Gonzalez.
Epidemiological characterisation of intestinal parasite infection in children. PhD candidate, Matias Scavuzzo.
Geospatial modelling of malnutrition in children and adolescents. PhD candidate, Micaela Campero.
Environmental variables associated with non-communicable diseases. Dr. Juan Diego Pinotti and Dr. Ximena Porcasi.
Challenges and gaps - RS
- Trade-off between different RS resolutions, the problem under study, the data and methods available
- Gaps in optical RS: clouds, shadows in optical RS (spatial and temporal interpolations)
- Need for corrections if high level data is not suitable (ARD)
- Limited access to VHR, LiDAR, Hyper-spectral (US$, not easy to scale yet)
- Investment and capacity building: huge volumes of data vs. limited bandwidth, storage and computational capacity (cloud computing, parallelisation | learning time and US$)
Field data will always be needed! :)
Challenges and gaps - Ecology and Health
- Missing baseline distribution information of hosts, vectors, infection
- Updating and digitisation of disease cases and intervention data, data still missing in large parts of the world
- Harmonisation of records at different levels, i.e., municipal, provincial, national
- Facilitating access to (aggregated) health data
- Political decision and resource allocation
Opportunities: low hanging fruits?
- SAR data to avoid clouds, e.g., SAOCOM to estimate soil moisture
- Open LiDAR data, e.g., GEDI onboard of ISS
- GEE vs open source solutions openEO.cloud, actinia, OpenPlains? ;-)
New missions: hyper-spectral for all
- A number of recent and upcoming missions for hyper-spectral data: PRISMA (recently made open), EnMap, CHIME, TIRS
Specialized cameras onboard drones
- Cheaper UAVs with different types of cameras, e.g. thermal multi- or hyper-spectral sensors to detect and count animals in inaccessible places
Thanks!
Unhealthy lab
References
Extra slides
App to count mosquito eggs in ovitraps pics
https://ovitrap-monitor.netlify.app/